






'''simple docstring'''


import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors




@require_tokenizers
class 				lowerCAmelCase       (				_A				,   unittest.TestCase ):







   """simple docstring"""







   _A							= MvpTokenizer
   _A							= MvpTokenizerFast
   _A							= True
   _A							= filter_roberta_detectors



   def  __magic_name__     (			self      )     -> Dict:
    super().setUp()
    __a     :						Any          =    [
        """l""",
        """o""",
        """w""",
        """e""",
        """r""",
        """s""",
        """t""",
        """i""",
        """d""",
        """n""",
        """\u0120""",
        """\u0120l""",
        """\u0120n""",
        """\u0120lo""",
        """\u0120low""",
        """er""",
        """\u0120lowest""",
        """\u0120newer""",
        """\u0120wider""",
        """<unk>""",
    ]
    __a     :						Tuple          =    dict(zip(snake_case_ , range(len(snake_case_      )      )      )      )
    __a     :						Optional[Any]          =    ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
    __a     :						List[Any]          =    {"""unk_token""": """<unk>"""}

    __a     :						List[Any]          =    os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file']      )
    __a     :						List[str]          =    os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file']      )
    with open(self.vocab_file , 'w' , encoding='utf-8'      ) as fp:
     fp.write(json.dumps(snake_case_      ) + '\n'      )
    with open(self.merges_file , 'w' , encoding='utf-8'      ) as fp:
     fp.write('\n'.join(snake_case_      )      )



   def  __magic_name__     (			self , **_A      )     -> List[Any]:
    kwargs.update(self.special_tokens_map      )
    return self.tokenizer_class.from_pretrained(self.tmpdirname , **snake_case_      )



   def  __magic_name__     (			self , **_A      )     -> int:
    kwargs.update(self.special_tokens_map      )
    return self.rust_tokenizer_class.from_pretrained(self.tmpdirname , **snake_case_      )



   def  __magic_name__     (			self , _A      )     -> Optional[int]:
    return "lower newer", "lower newer"



   @cached_property
   def  __magic_name__     (			self      )     -> Dict:
    return MvpTokenizer.from_pretrained('RUCAIBox/mvp'      )



   @cached_property
   def  __magic_name__     (			self      )     -> List[Any]:
    return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'      )



   @require_torch
   def  __magic_name__     (			self      )     -> Optional[Any]:
    __a     :						int          =    ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
    __a     :						Dict          =    [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     __a     :						int          =    tokenizer(snake_case_ , max_length=len(snake_case_      ) , padding=snake_case_ , return_tensors='pt'      )
     self.assertIsInstance(snake_case_ , snake_case_      )

     self.assertEqual((2, 9) , batch.input_ids.shape      )
     self.assertEqual((2, 9) , batch.attention_mask.shape      )
     __a     :						List[Any]          =    batch.input_ids.tolist()[0]
     self.assertListEqual(snake_case_ , snake_case_      )
     # Test that special tokens are reset



   @require_torch
   def  __magic_name__     (			self      )     -> Union[str, Any]:
    __a     :						int          =    ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     __a     :						List[Any]          =    tokenizer(snake_case_ , padding=snake_case_ , return_tensors='pt'      )
     # check if input_ids are returned and no labels
     self.assertIn('input_ids' , snake_case_      )
     self.assertIn('attention_mask' , snake_case_      )
     self.assertNotIn('labels' , snake_case_      )
     self.assertNotIn('decoder_attention_mask' , snake_case_      )



   @require_torch
   def  __magic_name__     (			self      )     -> Dict:
    __a     :						Dict          =    [
        """Summary of the text.""",
        """Another summary.""",
    ]
    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     __a     :						Any          =    tokenizer(text_target=snake_case_ , max_length=32 , padding='max_length' , return_tensors='pt'      )
     self.assertEqual(32 , targets['input_ids'].shape[1]      )



   @require_torch
   def  __magic_name__     (			self      )     -> Optional[Any]:
    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     __a     :						str          =    tokenizer(
         ['I am a small frog' * 1024, 'I am a small frog'] , padding=snake_case_ , truncation=snake_case_ , return_tensors='pt'      )
     self.assertIsInstance(snake_case_ , snake_case_      )
     self.assertEqual(batch.input_ids.shape , (2, 1024)      )



   @require_torch
   def  __magic_name__     (			self      )     -> Tuple:
    __a     :						Any          =    ["""A long paragraph for summarization."""]
    __a     :						Tuple          =    [
        """Summary of the text.""",
    ]
    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     __a     :						Dict          =    tokenizer(snake_case_ , text_target=snake_case_ , return_tensors='pt'      )
     __a     :						List[str]          =    inputs["""input_ids"""]
     __a     :						str          =    inputs["""labels"""]
     self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()      )
     self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()      )
     self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()      )
     self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()      )



   def  __magic_name__     (			self      )     -> Optional[int]:
    pass



   def  __magic_name__     (			self      )     -> int:
    for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
     with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
      __a     :						Tuple          =    self.rust_tokenizer_class.from_pretrained(snake_case_ , **snake_case_      )
      __a     :						Tuple          =    self.tokenizer_class.from_pretrained(snake_case_ , **snake_case_      )
      __a     :						Union[str, Any]          =    """A, <mask> AllenNLP sentence."""
      __a     :						List[Any]          =    tokenizer_r.encode_plus(snake_case_ , add_special_tokens=snake_case_ , return_token_type_ids=snake_case_      )
      __a     :						Union[str, Any]          =    tokenizer_p.encode_plus(snake_case_ , add_special_tokens=snake_case_ , return_token_type_ids=snake_case_      )

      # token_type_ids should put 0 everywhere
      self.assertEqual(sum(tokens_r['token_type_ids']      ) , sum(tokens_p['token_type_ids']      )      )

      # attention_mask should put 1 everywhere, so sum over length should be 1
      self.assertEqual(
          sum(tokens_r['attention_mask']      ) / len(tokens_r['attention_mask']      ) , sum(tokens_p['attention_mask']      ) / len(tokens_p['attention_mask']      ) , )

      __a     :						List[str]          =    tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']      )
      __a     :						Optional[Any]          =    tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']      )

      # Rust correctly handles the space before the mask while python doesnt
      self.assertSequenceEqual(tokens_p['input_ids'] , [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]      )
      self.assertSequenceEqual(tokens_r['input_ids'] , [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]      )

      self.assertSequenceEqual(
          snake_case_ , ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']      )
      self.assertSequenceEqual(
          snake_case_ , ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']      )






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
							import torch

							from transformers.generation import DisjunctiveConstraint

@require_torch
class 							_UpperCamelCase					(       unittest.TestCase	):






			'''simple docstring'''

			def    lowerCAmelCase__						(							self			:							Dict   ):
							# For consistency across different places the DisjunctiveConstraint is called,
							# dc.token_ids is a list of integers. It is also initialized only by integers.

							UpperCamelCase_:				Optional[int]						=    [[1, 2, 4], [1, 2, 3, 4]]
							UpperCamelCase_:				List[str]						=    DisjunctiveConstraint(snake_case_   )
							self.assertTrue(isinstance(dc.token_ids    ,						snake_case_   )   )

							with self.assertRaises(snake_case_   ):
											DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]   )   )

							with self.assertRaises(snake_case_   ):
											DisjunctiveConstraint([torch.LongTensor([1, 2, 4]   ), torch.LongTensor([1, 2, 3, 4, 5]   )]   )

			def    lowerCAmelCase__						(							self			:							Optional[Any]   ):
							# We can't have constraints that are complete subsets of another. This leads to a preverse
							# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
							# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
							# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
							# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
							UpperCamelCase_:				Dict						=    [[1, 2], [1, 2, 3, 4]]

							with self.assertRaises(snake_case_   ):
											DisjunctiveConstraint(snake_case_   )  # fails here

			def    lowerCAmelCase__						(							self			:							int   ):
							UpperCamelCase_:				Dict						=    [[1, 2, 3], [1, 2, 4]]

							UpperCamelCase_:				Dict						=    DisjunctiveConstraint(snake_case_   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				str						=    dc.update(1   )
							UpperCamelCase_:				Union[str, Any]						=    stepped is True and completed is False and reset is False
							self.assertTrue(snake_case_   )
							self.assertTrue(not dc.completed   )
							self.assertTrue(dc.current_seq == [1]   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				Optional[int]						=    dc.update(2   )
							UpperCamelCase_:				Tuple						=    stepped is True and completed is False and reset is False
							self.assertTrue(snake_case_   )
							self.assertTrue(not dc.completed   )
							self.assertTrue(dc.current_seq == [1, 2]   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				Optional[int]						=    dc.update(3   )
							UpperCamelCase_:				Union[str, Any]						=    stepped is True and completed is True and reset is False
							self.assertTrue(snake_case_   )
							self.assertTrue(dc.completed   )  # Completed!
							self.assertTrue(dc.current_seq == [1, 2, 3]   )




			def    lowerCAmelCase__						(							self			:							List[str]   ):
							UpperCamelCase_:				Optional[int]						=    [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

							UpperCamelCase_:				Tuple						=    DisjunctiveConstraint(snake_case_   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				Dict						=    dc.update(1   )
							self.assertTrue(not dc.completed   )
							self.assertTrue(dc.current_seq == [1]   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				Union[str, Any]						=    dc.update(2   )
							self.assertTrue(not dc.completed   )
							self.assertTrue(dc.current_seq == [1, 2]   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				Any						=    dc.update(4   )
							self.assertTrue(not dc.completed   )
							self.assertTrue(dc.current_seq == [1, 2, 4]   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				int						=    dc.update(5   )
							self.assertTrue(dc.completed   )  # Completed!
							self.assertTrue(dc.current_seq == [1, 2, 4, 5]   )

							dc.reset()

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				int						=    dc.update(1   )
							self.assertTrue(not dc.completed   )
							self.assertTrue(dc.remaining() == 3   )
							self.assertTrue(dc.current_seq == [1]   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				Tuple						=    dc.update(2   )
							self.assertTrue(not dc.completed   )
							self.assertTrue(dc.remaining() == 2   )
							self.assertTrue(dc.current_seq == [1, 2]   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				List[str]						=    dc.update(5   )
							self.assertTrue(dc.completed   )  # Completed!
							self.assertTrue(dc.remaining() == 0   )
							self.assertTrue(dc.current_seq == [1, 2, 5]   )






